A Nonlinear Splitting Algorithm for Systems of Partial Differential Equations with self-Diffusion
This work addresses the challenge of efficiently and accurately approximating biological models with self-diffusion, which is a nonlinear term complicating numerical solutions.
The paper presents a new nonlinear splitting algorithm for systems of partial differential equations with self-diffusion, providing stability and convergence criteria, and illustrating results with numerical examples.
Systems of reaction-diffusion equations are commonly used in biological models of food chains. The populations and their complicated interactions present numerous challenges in theory and in numerical approximation. In particular, self-diffusion is a nonlinear term that models overcrowding of a particular species. The nonlinearity complicates attempts to construct efficient and accurate numerical approximations of the underlying systems of equations. In this paper, a new nonlinear splitting algorithm is designed for a partial differential equation that incorporates self-diffusion. We present a general model that incorporates self-diffusion and develop a numerical approximation. The numerical analysis of the approximation provides criteria for stability and convergence. Numerical examples are used to illustrate the theoretical results.